1. Field of the Invention
The present invention relates to systems and methods for monitoring machinery in order to diagnose and predict failure of an element thereof.
2. Description of Related Art
The use of neural networks to receive and process data from a plurality of sensors positioned and adapted for monitoring operating parameters of a device is known. For example, U.S. Pat. No. 6,301,572, co-owned with the present invention, and the contents of which are incorporated herein by reference, discloses a system and method for tracking the long-term performance of a vibrating body such as a turbine that employs a fuzzy adaptive resonance theory neural network.
U.S. Pat. No. 6,741,974, co-owned with the present invention, and the contents of which are incorporated herein by reference, discloses a system and method for incorporating machine learning and automatic adaptation to respond to changing environmental conditions. The specific systems and methods taught therein incorporate genetic algorithms, learning classifier systems, and agent technology to form a complex adaptive system.
U.S. Pat. No. 7,277,823, also co-owned with the present invention, and the contents of which are incorporated herein by reference, discloses a system and method for monitoring an operating device that employs a neural network that receives and processes sensor data for detecting and predicting anomalies in the operating device parameters.
It would be desirable to provide a system and method for performing real-time condition-based analysis on equipment, integrating the above-referenced technologies, to achieve diagnostic and prognostic outputs indicative of the status of the equipment based upon sensor data.